{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "432da7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f6eb274",
   "metadata": {},
   "source": [
    "### Robot Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65b9e7db",
   "metadata": {},
   "outputs": [],
   "source": [
    "radius = 20\n",
    "noSegments = 1\n",
    "segLength = 340"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ee46456",
   "metadata": {},
   "source": [
    "### Transformation Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fdfee488",
   "metadata": {},
   "outputs": [],
   "source": [
    "def actuator_to_configSpace(Tl):\n",
    "    '''\n",
    "    Perform Transformation from actuator to configuration space for 4 tendon continuum robot\n",
    "    \n",
    "    Input: \n",
    "        Tl -- list consisting of tendon lengths\n",
    "    Output:\n",
    "        k -- Robot Curvature\n",
    "        l -- Robot length\n",
    "        phi -- Bending angle\n",
    "    '''\n",
    "    pi = math.pi\n",
    "    if Tl[2]==Tl[0] and Tl[3]==Tl[1] and Tl[2]==Tl[3]:\n",
    "        phi = 0\n",
    "    elif Tl[2]==Tl[0] and Tl[3]==Tl[1]:\n",
    "        phi = -pi/2\n",
    "    elif Tl[2]==Tl[0]:\n",
    "        phi = pi/2\n",
    "    else:\n",
    "        phi = math.atan((Tl[3]-Tl[1])/(Tl[2]-Tl[0]))\n",
    "\n",
    "    if Tl[2]==Tl[0] and Tl[3]==Tl[1] and Tl[2]==Tl[3]:\n",
    "        k = 0\n",
    "    elif Tl[3]==Tl[1] and Tl[2]==Tl[0]:\n",
    "        k = (Tl[0]-3*Tl[1]+Tl[2]+Tl[3])*math.sqrt(2)/(radius*sum(Tl))\n",
    "    elif Tl[3]==Tl[1]:\n",
    "        k = (Tl[1]-3*Tl[0]+Tl[2]+Tl[3])*math.sqrt((Tl[3]-Tl[1])**2+(Tl[2]-Tl[0])**2)/(radius*sum(Tl)*(Tl[2]-Tl[0]))\n",
    "    else:\n",
    "        k = (Tl[0]-3*Tl[1]+Tl[2]+Tl[3])*math.sqrt((Tl[3]-Tl[1])**2+(Tl[2]-Tl[0])**2)/(radius*sum(Tl)*(Tl[3]-Tl[1]))\n",
    "\n",
    "    l = sum(Tl)/4\n",
    "    return k,l,phi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eae89ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rotation(ax=[0,0,1],tht=0):\n",
    "    [x,y,z] = ax\n",
    "    c = math.cos(tht)\n",
    "    s = math.sin(tht)\n",
    "    R = [[(1-c)*x**2+c,(1-c)*x*y-s*z,(1-c)*x*z+s*y],\n",
    "         [(1-c)*x*y+s*z,(1-c)*y**2+c,(1-c)*z*y-s*x],\n",
    "         [(1-c)*x*z-s*y,(1-c)*z*y+s*x,(1-c)*z**2+c]]\n",
    "    R= np.array(R)\n",
    "    return R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "999ec981",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform(rot,trans):\n",
    "    Tr = np.append(rot,trans,axis=1)\n",
    "    Tr = np.append(Tr,[[0,0,0,1]],axis=0)\n",
    "    return Tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2799fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def config_to_taskSpace(k,l,phi):\n",
    "    \n",
    "    p0 = [[0],[0],[0]]\n",
    "    if k == 0:\n",
    "        p = [[0],[0],[l]]\n",
    "    else:\n",
    "        p = [[(1-math.cos(k*l))/k],[0],[math.sin(k*l)/k]]\n",
    "\n",
    "    Tbr = transform(rotation(tht=phi),p0)\n",
    "    theta = k*l\n",
    "    Trt = transform(rotation(ax=[0,1,0],tht=theta),p)\n",
    "    Ttm = transform(rotation(tht=-phi),p0)\n",
    "    \n",
    "    Tf = np.matmul(Tbr,Trt)\n",
    "    Tf = np.matmul(Tf,Ttm)\n",
    "    return Tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf229bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rotation_quaternion(R):\n",
    "    \n",
    "    tr = np.trace(R)\n",
    "    \n",
    "    if tr>0:\n",
    "        S = math.sqrt(tr+1)*2\n",
    "        qw = 0.25 * S\n",
    "        qx = (R[2][1] - R[1][2])/S\n",
    "        qy = (R[0][2] - R[2][0])/S \n",
    "        qz = (R[1][0] - R[0][1])/S\n",
    "    elif (R[0][0] > R[1][1]) and (R[0][0] > R[2][2]):\n",
    "        S = math.sqrt(1+R[0][0]-R[1][1]-R[2][2])*2\n",
    "        qw = (R[2][1] - R[1][2])/S\n",
    "        qx = 0.25 * S\n",
    "        qy = (R[0][1] + R[1][0])/S\n",
    "        qz = (R[0][2] + R[2][0])/S \n",
    "    elif (R[1][1] > R[2][2]):\n",
    "        S = math.sqrt(1+R[1][1]-R[0][0]-R[2][2])*2\n",
    "        qw = (R[2][0] - R[0][2])/S\n",
    "        qx = (R[0][1] + R[1][0])/S\n",
    "        qy = 0.25 * S\n",
    "        qz = (R[1][2] + R[2][1])/S \n",
    "    else:\n",
    "        S = math.sqrt(1+R[2][2]-R[0][0]-R[1][1])*2\n",
    "        qw = (R[1][0] - R[0][1])/S\n",
    "        qx = (R[0][2] + R[2][0])/S \n",
    "        qy = (R[2][1] + R[1][2])/S\n",
    "        qz = 0.25 * S \n",
    "    \n",
    "    return [qw,qx,qy,qz]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3921329",
   "metadata": {},
   "source": [
    "### Input Parameter Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "48725435",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pi = math.pi\n",
    "# K = np.arange(0,pi/(2*segLength),0.00004)\n",
    "# l = segLength\n",
    "# PHI= np.arange(0,2*pi,0.01)\n",
    "# position = []\n",
    "# param = []\n",
    "# for i in range(K.shape[0]):\n",
    "#     for j in range(PHI.shape[0]):\n",
    "#         T = config_to_taskSpace(K[i],l,PHI[j])\n",
    "#         param.append([K[i],l,PHI[j]])\n",
    "#         position.append(T[:3,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "22866848",
   "metadata": {},
   "outputs": [],
   "source": [
    "position=[]\n",
    "orientation = []\n",
    "config=[]\n",
    "pi = math.pi\n",
    "Actcuator = []\n",
    "normal = [340,340,340,340]\n",
    "for l1 in range(segLength-30,segLength+31):\n",
    "    for l2 in range(segLength-30,segLength+31):\n",
    "        for l3 in range(segLength-30,segLength+31):\n",
    "            for l4 in range(segLength-30,segLength+31):\n",
    "                #value = np.random.randint(-30,30,4)\n",
    "                length = [l1,l2,l3,l4]\n",
    "#                 if len(Actcuator)!=0 and (length == Actcuator).any():\n",
    "#                     print(\"Helo\")\n",
    "#                     continue\n",
    "                k,l,phi = actuator_to_configSpace(length)\n",
    "                if abs(k*l)>pi/2:\n",
    "                    continue\n",
    "                T = config_to_taskSpace(k,l,phi)\n",
    "                quat = rotation_quaternion(T[0:3,0:3])\n",
    "                orientation.append(quat)\n",
    "                Actcuator.append(length)\n",
    "                config.append([k,l,phi])\n",
    "                position.append(T[:3,-1])\n",
    "                if phi>=0:\n",
    "                    phi+=math.pi\n",
    "                elif phi<0:\n",
    "                    phi = math.pi - abs(phi)\n",
    "                T = config_to_taskSpace(k,l,phi)\n",
    "                quat = rotation_quaternion(T[0:3,0:3])\n",
    "                orientation.append(quat)\n",
    "                Actcuator.append(length)\n",
    "                config.append([k,l,phi])\n",
    "                position.append(T[:3,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46997046",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputLength = np.array(Actcuator)\n",
    "position = np.array(position)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfe6af66",
   "metadata": {},
   "source": [
    "### Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "85bb8124",
   "metadata": {},
   "outputs": [],
   "source": [
    "xdata = []\n",
    "ydata = []\n",
    "zdata = []\n",
    "for i in range(len(position)):\n",
    "    if position[i][2]<400:\n",
    "        xdata.append(position[i][0])\n",
    "        ydata.append(position[i][1])\n",
    "        zdata.append(position[i][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff00c8e6",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 641. MiB for an array with shape (21007890, 4) and data type float64",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2208\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2209\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2210\u001b[1;33m                 result = print_method(\n\u001b[0m\u001b[0;32m   2211\u001b[0m                     \u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2212\u001b[0m                     \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1639\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1641\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    507\u001b[0m             \u001b[1;33m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[1;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m         \"\"\"\n\u001b[1;32m--> 509\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    510\u001b[0m         mpl.image.imsave(\n\u001b[0;32m    511\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"upper\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    405\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[0;32m    406\u001b[0m               else nullcontext()):\n\u001b[1;32m--> 407\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    408\u001b[0m             \u001b[1;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    409\u001b[0m             \u001b[1;31m# don't forget to call the superclass.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1862\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1863\u001b[1;33m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[0;32m   1864\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[0;32m   1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    129\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    443\u001b[0m                             for axis in self._get_axis_list()) + 1\n\u001b[0;32m    444\u001b[0m         for i, col in enumerate(\n\u001b[1;32m--> 445\u001b[1;33m                 sorted(self.collections,\n\u001b[0m\u001b[0;32m    446\u001b[0m                        \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_3d_projection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m                        reverse=True)):\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(col)\u001b[0m\n\u001b[0;32m    444\u001b[0m         for i, col in enumerate(\n\u001b[0;32m    445\u001b[0m                 sorted(self.collections,\n\u001b[1;32m--> 446\u001b[1;33m                        \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_3d_projection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    447\u001b[0m                        reverse=True)):\n\u001b[0;32m    448\u001b[0m             \u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzorder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzorder_offset\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\mpl_toolkits\\mplot3d\\art3d.py\u001b[0m in \u001b[0;36mdo_3d_projection\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    522\u001b[0m         \u001b[0mvps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn_stack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    523\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 524\u001b[1;33m         \u001b[0mfcs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_rgba_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfcs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_alpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    525\u001b[0m         \u001b[0mecs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_rgba_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mecs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_alpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    526\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\keras_env\\lib\\site-packages\\matplotlib\\colors.py\u001b[0m in \u001b[0;36mto_rgba_array\u001b[1;34m(c, alpha)\u001b[0m\n\u001b[0;32m    299\u001b[0m             \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m1.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    300\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 301\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    302\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    303\u001b[0m                 \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 641. MiB for an array with shape (21007890, 4) and data type float64"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter3D(xdata, ydata, zdata, c=zdata);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88cb5c7f",
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 641. MiB for an array with shape (21007890, 4) and data type float64",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-0fcff7f20894>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0morientation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 641. MiB for an array with shape (21007890, 4) and data type float64"
     ]
    }
   ],
   "source": [
    "orientation = np.array(orientation)\n",
    "orientation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6533d45",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.concatenate((position,inputLength),axis=1)\n",
    "df = pd.DataFrame(arr) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "047c7de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"CompleteWorkspace_quat.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "712a33e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = np.random.randint(0,position.shape[0]-1,20000)\n",
    "positionRandom = position[index]\n",
    "LengthRandom = inputLength[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "84f90a9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "xdata = []\n",
    "ydata = []\n",
    "zdata = []\n",
    "for i in range(len(positionRandom)):\n",
    "    if positionRandom[i][2]<400:\n",
    "        xdata.append(positionRandom[i][0])\n",
    "        ydata.append(positionRandom[i][1])\n",
    "        zdata.append(positionRandom[i][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e0adbd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter3D(xdata, ydata, zdata, c=zdata);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55b8ce53",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.concatenate((positionRandom,LengthRandom),axis=1)\n",
    "df = pd.DataFrame(arr) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "61ff75e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"MLDataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bdb6e76",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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